Best AI Scripts 2026

Production-grade ASR pipeline using OpenAI Whisper or faster-whisper with VAD-based chunking, speaker timestamp alignment, and SRT/VTT subtitle export. Handles long-form audio via sliding window segmentation and automatic language detection.

Bootstraps a production-ready object detection workflow using YOLOv8 or RT-DETR, including webcam/video stream ingestion, NMS post-processing, and annotation overlay rendering. Outputs annotated frames and a structured JSON detections log suitable for downstream analytics.

Computes and visualizes feature importance using SHAP (TreeExplainer, KernelExplainer), permutation importance, and Boruta for any scikit-learn-compatible model. Generates HTML dashboards with global/local explanations, feature interaction heatmaps, and ranked importance tables exportable to CSV.

Production-ready FastAPI template for AI-powered REST APIs, with pre-wired OpenAI/Anthropic client, async streaming endpoints, JWT authentication, rate limiting, structured logging, and OpenAPI docs. Includes Docker Compose stack with Redis rate-limit store and Prometheus metrics.

End-to-end fraud detection pipeline combining XGBoost/LightGBM with isolation forest anomaly detection, handling severe class imbalance via SMOTE-Tomek resampling and cost-sensitive learning. Includes a real-time scoring API with sub-10ms latency, feature drift monitoring, and an explainability layer for dispute resolution.

End-to-end image classification pipeline that handles dataset loading, preprocessing, model inference, and result export using PyTorch and torchvision. Supports batch inference against any Hugging Face ViT or ResNet checkpoint with configurable confidence thresholds.

Fine-tunes language models using Low-Rank Adaptation (LoRA) for parameter-efficient training. Handles dataset preparation, adapter configuration, training loop with gradient accumulation, evaluation, and adapter merging for deployment-ready models.

Multi-engine OCR pipeline that routes documents to Tesseract, PaddleOCR, or a cloud OCR API based on image quality heuristics. Outputs structured JSON with bounding boxes, confidence scores, and reading-order-sorted text blocks ready for downstream NLP.

Runs Segment Anything Model (SAM 2) or Mask2Former on image batches, producing per-pixel segmentation masks with class labels and confidence scores. Includes utilities for mask overlay visualization and RLE-encoded mask export compatible with COCO annotation format.

Runs automated data quality checks on tabular datasets using Great Expectations, producing a profiling report with schema validation, distribution drift alerts, and referential integrity checks. Integrates with CI/CD pipelines to block model training when data quality gates fail.

Detects and redacts personally identifiable information from text and structured data using Microsoft Presidio with configurable entity recognizers for GDPR and HIPAA compliance. Supports reversible pseudonymization with a secure vault for re-identification by authorized parties.

Minimal RAG pipeline script for getting started with retrieval-augmented generation. Sets up document loading, text splitting, embedding generation, and vector store indexing with a simple query interface for prototyping and learning.

Identifies and segments individual speakers in multi-party audio recordings using pyannote.audio, then merges speaker labels with Whisper transcriptions to produce a turn-by-turn conversation transcript. Supports overlapping speech detection and speaker enrollment for known identities.

Scaffolds a production chatbot combining Rasa for intent classification and dialogue management with an LLM fallback for open-domain questions, deployable to Web, WhatsApp, and Slack via a single channel adapter layer. Includes conversation analytics, entity extraction training data tooling, and a Streamlit testing playground.

Implements a GraphRAG pattern that stores document entities and relationships in Neo4j, then retrieves contextually relevant subgraphs at query time before passing them to an LLM. Includes automatic entity extraction with spaCy, relationship inference, and a Cypher query generator.

Builds a reverse image search engine using CLIP embeddings indexed in Qdrant or Weaviate, supporting text-to-image and image-to-image queries. Includes a FastAPI server, automatic re-ranking with a cross-encoder, and a Gradio demo interface.

Packages a trained ML model into a serverless function on AWS Lambda, Modal, or Google Cloud Run, handling cold-start optimization, dependency layering, and auto-scaling configuration. Includes health-check endpoints, structured logging, and a GitHub Actions workflow for automated rollout.

Builds a two-stage recommendation engine using a two-tower neural retrieval model for candidate generation and an LLM-based cross-encoder for re-ranking, with a Feast feature store for real-time user context. Supports cold-start via content-based fallback and A/B test scaffolding for algorithm experimentation.

Optimizes PyTorch or TensorFlow models for edge deployment by applying INT8/FP16 quantization, ONNX export, and TFLite conversion with platform-specific tuning for ARM/NPU targets. Benchmarks latency, memory, and accuracy trade-offs across optimization strategies and generates a deployment report.

Deploys a language model as an OpenAI-compatible API server using vLLM. Configures PagedAttention for memory efficiency, continuous batching for throughput, tensor parallelism for multi-GPU setups, and health monitoring endpoints.

WebSocket server that proxies token-by-token LLM streaming to multiple simultaneous clients, with connection lifecycle management, heartbeat keep-alives, and per-session context persistence. Supports fan-out broadcasting for collaborative AI sessions and reconnection with message replay.

Applies Deep Feature Synthesis via Featuretools and AutoFeat to automatically generate hundreds of candidate features from relational tabular data, then prunes them using mutual information and SHAP-based importance filters. Produces a reproducible feature pipeline serializable to scikit-learn format.

Ingests social media feeds, reviews, and support tickets in near-real-time, scores sentiment at entity and aspect level using a fine-tuned RoBERTa model, and renders a live Streamlit dashboard with trend charts, topic clustering, and configurable alert thresholds for brand-crisis detection.

Cleans and normalizes text data for LLM consumption by removing HTML artifacts, fixing encoding issues, standardizing whitespace, deduplicating near-identical entries, and filtering low-quality content based on configurable quality heuristics.

Sets up a zero-shot voice cloning pipeline using Coqui XTTS-v2 or Tortoise-TTS, requiring only a 3-second reference audio clip to synthesize new speech in the target voice. Includes a FastAPI inference server, audio quality normalization, and speaker embedding export for reuse.